3-D QSAR analysis of steroid/protein interactions: The use of difference maps

Norinder, Ulf

doi:10.1007/bf00125662

Cited by 18 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The validation set was selected in the same manner as the training set, that is, by using an experimental design called a fractional factorial design (FFD) [ 13,141. Originating from a principal component analysis (PCA) of chemical characterization data in the above mentioned work [lo], three principal components (PCs) were used to construct the design. PCs are ideal for building FFDs because they are few and orthogonal to each other [15,16]. To select the validation set, a Z3-' FFD was constructed ( Table 2).…”

Section: Selection Of the Validation Setmentioning

confidence: 99%

See 1 more Smart Citation

External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

Eriksson

Jönsson

Berglind

1993

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

The validation of the predictive capability of a quantitative structure-activity relationship (QSAR) is a significant step toward the construction of a reliable model. This point is discussed and illustrated with data for a class of halogenated aliphatic hydrocarbons. For this class of compounds, a QSAR concerning their acute toxicity toward rat was recently published. This QSAR is verified in this study by selecting and testing an external validation set comprising six compounds. The QSAR is also used for predicting the acute toxicity of 38 nontested members of this class.

show abstract

Section: Selection Of the Validation Setmentioning

confidence: 99%

“…In the selection procedure, main emphasis was given to select compounds with matching scores of PC1 and PC2, because these two PCs accounted for most of the chemical variation. For a deeper discussion, see references[9,15,16].…”

mentioning

confidence: 99%

External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

Eriksson

Jönsson

Berglind

1993

Enviro Toxic and Chemistry

View full text Add to dashboard Cite

show abstract

“…A quick look at such a plot may help the researcher to choose an appropriate statistical method, whether linear (like PLS), or nonlinear (like clustering). Such a plot is a useful supplement to 3D maps of the molecular fields generated by CoMFA and by related methods [25].…”

Section: Molecular Modeling and Statistical Methodsmentioning

confidence: 99%

Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA

Bush

Nachbar

1993

J Computer-Aided Mol Des

404

253

View full text Add to dashboard Cite

Three-dimensional molecular modeling can provide an unlimited number m of structural properties. Comparative Molecular Field Analysis (CoMFA), for example, may calculate thousands of field values for each model structure. When m is large, partial least squares (PLS) is the statistical method of choice for fitting and predicting biological responses. Yet PLS is usually implemented in a property-based fashion which is optimal only for small m. We describe here a sample-based formulation of PLS which can be used to fit any single response (bioactivity). SAMPLS reduces all explanatory data to the pairwise 'distances' among n samples (molecules), or equivalently to an n-by-n covariance matrix C. This matrix, unmodified, can be used to fit all PLS components. Furthermore, SAMPLS will validate the model by modern resampling techniques, at a cost independent of m. We have implemented SAMPLS as a Fortran program and have reproduced conventional and cross-validated PLS analyses of data from two published studies. Full (leave-each-out) cross-validation of a typical CoMFA takes 0.2 CPU s. SAMPLS is thus ideally suited to structure-activity analysis based on CoMFA fields or bonded topology. The sample-distance formulation also relates PLS to methods like cluster analysis and nonlinear mapping, and shows how drastically PLS simplifies the information in CoMFA fields.

show abstract

“…It employs statistical techniques to correlate biological endpoints with molecular features in terms of calculated interaction energies with virtual probes and interactive graphics can be used subsequently to interpret the results. Notably, the CoMFA method, originally developed with a set of steroids [ 5 ], has frequently been applied to numerous steroids and various biological endpoints [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], so that it appeared straightforward to apply it to the present set of alkaloids with pregnane and pregnene scaffolds. The compounds under study can roughly be grouped into (a) androstane and androst-5-ene derivatives with 3-amino- and 17β-acetyl substituents, (b) pregn-5-ene derivatives with a 3-amino substituent and an additional amino group connecting C-20 and C-18 forming a pyrrolidine ring and (c) pregnene derivatives with a C-3- or C-7-oxo substituent and a C-18/C-20 pyrrolidine or pyrroline ring similar to (b).…”

Section: Introductionmentioning

confidence: 99%

A 3D-QSAR Study on the Antitrypanosomal and Cytotoxic Activities of Steroid Alkaloids by Comparative Molecular Field Analysis

et al. 2018

View full text Add to dashboard Cite

As part of our research for new leads against human African trypanosomiasis (HAT), we report on a 3D-QSAR study for antitrypanosomal activity and cytotoxicity of aminosteroid-type alkaloids recently isolated from the African medicinal plant Holarrhena africana A. DC. (Apocynaceae), some of which are strong trypanocides against Trypanosoma brucei rhodesiense (Tbr), with low toxicity against mammalian cells. Fully optimized 3D molecular models of seventeen congeneric Holarrhena alkaloids were subjected to a comparative molecular field analysis (CoMFA). CoMFA models were obtained for both, the anti-Tbr and cytotoxic activity data. Model performance was assessed in terms of statistical characteristics (R2, Q2, and P2 for partial least squares (PLS) regression, internal cross-validation (leave-one-out), and external predictions (test set), respectively, as well as the corresponding standard deviation error in prediction (SDEP) and F-values). With R2 = 0.99, Q2 = 0.83 and P2 = 0.79 for anti-Tbr activity and R2 = 0.94, Q2 = 0.64, P2 = 0.59 for cytotoxicity against L6 rat skeletal myoblasts, both models were of good internal and external predictive power. The regression coefficients of the models representing the most prominent steric and electrostatic effects on anti-Tbr and for L6 cytotoxic activity were translated into contour maps and analyzed visually, allowing suggestions for possible modification of the aminosteroids to further increase the antitrypanosomal potency and selectivity. Very interestingly, the 3D-QSAR model established with the Holarrhena alkaloids also applied to the antitrypanosomal activity of two aminocycloartane-type compounds recently isolated by our group from Buxus sempervirens L. (Buxaceae), which indicates that these structurally similar natural products share a common structure–activity relationship (SAR) and, possibly, mechanism of action with the Holarrhena steroids. This 3D-QSAR study has thus resulted in plausible structural explanations of the antitrypanosomal activity and selectivity of aminosteroid- and aminocycloartane-type alkaloids as an interesting new class of trypanocides and may represent a starting point for lead optimization.

show abstract

3-D QSAR analysis of steroid/protein interactions: The use of difference maps

Abstract: The approach to use difference maps depicting differences in 3-D QSAR coefficients of different biological activities to analyze and monitor selectivity is described.

Cited by 18 publications

References 7 publications

External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

External validation of a QSAR for the acute toxicity of halogenated aliphatic hydrocarbons

Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA

A 3D-QSAR Study on the Antitrypanosomal and Cytotoxic Activities of Steroid Alkaloids by Comparative Molecular Field Analysis

Contact Info

Product

Resources

About